Flywheel energy storage system support method based on data center power switching

Abstract

The application discloses a flywheel energy storage system supporting method based on data center power switching and belongs to the technical field of power supply of power systems, which comprises the following steps: collecting real-time impedance parameters of each electrical equipment in a power grid through a preset impedance detection node to judge the power supply state of a main power supply and generating power topology state parameters; generating flywheel rotor dynamic torque compensation parameters according to the power topology state parameters and a preset torque threshold value, controlling the output of a preset multi-stage power switching matrix to synchronize phase parameters; and generating power distribution weight parameters based on the synchronized phase parameters and a preset load priority queue and updating the state parameters of the flywheel energy storage system. Through real-time impedance parameter monitoring of the power grid topology state, dynamic adjustment of the flywheel torque compensation parameters and multi-stage power synchronization phase control, and the power weight distribution of the load priority queue, the application can realize millisecond-level response, phase continuous transition and power supply continuity guarantee of key loads in the power supply switching process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power supply of power system, and particularly relates to a flywheel energy storage system supporting method based on data center power switching. BACKGROUND

[0002] With the increasing demand for power supply continuity in data centers, flywheel energy storage systems have gradually become one of the mainstream technologies for ensuring emergency power supply of critical loads due to their high power density and fast response characteristics. In the prior art, data center power switching mainly relies on a combination scheme of dual power supply and uninterruptible power supply (UPS), and the switching of main and standby power supply is realized through a static switching switch, and the flywheel energy storage system is connected to the power supply network as a transient power supplement device.

[0003] The current technology triggers the power switching operation by monitoring the amplitude change of voltage or current, usually adopts the logic judgment of fixed threshold, and relies on the inertia release of flywheel energy storage to maintain short-time power supply during the switching process. The detection of power supply topology state is mostly limited to the breakpoint of the main circuit, the input of standby power supply relies on preset time sequence control, and the power distribution after switching adopts the fixed proportion of shunt strategy.

[0004] However, the prior art cannot realize real-time sensing of the topology impedance change of the power grid, resulting in that the switching decision lags behind the actual fault occurrence time; the flywheel torque compensation mechanism has insufficient adaptability to the dynamic demand of the power grid, which easily causes phase asynchronization in the switching process; in addition, the power distribution lacks dynamic response ability to the load priority and instantaneous demand, and it is difficult to avoid the power supply fluctuation of critical loads. SUMMARY

[0005] To solve the above problems, the present application provides a flywheel energy storage system supporting method based on data center power switching, which can realize millisecond-level response, phase continuous transition and power supply continuity guarantee of critical loads in the power switching process by real-time impedance parameter monitoring of power grid topology state, dynamic adjustment of flywheel torque compensation parameters and multi-stage power synchronous phase control, combined with power weight distribution of load priority queue.

[0006] The above object can be realized by the following scheme:

[0007] A method for supporting a flywheel energy storage system based on data center power switching includes: collecting real-time impedance parameters of various electrical devices in the power grid through preset impedance detection nodes; determining the power supply status of the main power supply based on the real-time impedance parameters to generate power topology status parameters; generating flywheel rotor dynamic torque compensation parameters based on the power topology status parameters and a preset torque threshold; controlling a preset multi-level power switching matrix to output synchronization phase parameters based on the dynamic torque compensation parameters; generating power allocation weight parameters based on the synchronization phase parameters and a preset load priority queue, and updating the status parameters of the flywheel energy storage system.

[0008] Optionally, generating power topology state parameters includes: collecting real-time impedance parameters of each electrical device in the power grid through preset impedance detection nodes; determining the power supply status of the main power supply based on the real-time impedance parameters to obtain a power supply status determination result; acquiring real-time impedance parameters of multiple preset impedance detection nodes to form a topology impedance map; and generating power topology state parameters based on the topology impedance map and the power supply status determination result.

[0009] Optionally, generating the flywheel rotor dynamic torque compensation parameters includes: calculating the theoretical torque demand parameters based on the power topology state parameters; collecting real-time angular velocity data of the flywheel rotor, performing coupled compensation calculations on the real-time angular velocity data and the theoretical torque demand parameters to generate a deviation compensation coefficient; and performing closed-loop correction on the deviation compensation coefficient to obtain the dynamic torque compensation parameters.

[0010] Optionally, the step of calculating the theoretical torque demand parameters based on the power topology state parameters includes: acquiring historical load fluctuation parameters and historical power demand parameters to establish a historical dataset; using real-time load fluctuation parameters as input and real-time power demand parameters as output, using the historical dataset to establish and train a neural network model to obtain a real-time power demand prediction model; acquiring real-time load fluctuation parameters and inputting them into the real-time power demand prediction model to obtain real-time power demand parameters; using the real-time power demand parameters as constraints, calculating the theoretical torque demand parameters using the power topology state parameters.

[0011] Optionally, controlling the output synchronization phase parameters of the preset multi-level power switching matrix based on the dynamic torque compensation parameters includes: adjusting the carrier phase shift timing based on the dynamic torque compensation parameters to generate a synchronization phase control signal; broadcasting the synchronization phase control signal to the node terminals of the preset multi-level power switching matrix to output the synchronization phase parameters.

[0012] Optionally, the method of broadcasting the synchronization phase control signal to the node terminal of a preset multi-level power switching matrix and outputting the synchronization phase parameters includes: based on the power supply status judgment result, controlling the switching of the redundant bridge arms of the main circuit and the backup power supply to obtain a switching result; based on the switching result, using carrier phase-shift PWM control to adjust the duty cycle of the synchronization phase control signal and synchronizing it to the node terminal of the preset multi-level power switching matrix to output the synchronization phase parameters.

[0013] Optionally, the step of generating power allocation weight parameters based on the synchronization phase parameters and the preset load priority queue, and updating the state parameters of the flywheel energy storage system includes: creating a timestamp-based load priority queue and marking the response level parameters of the core equipment; obtaining the instantaneous power demand, calculating the power allocation weight parameters according to the response level parameters and the instantaneous power demand; and updating the state parameters of the flywheel energy storage system based on the power allocation weight parameters.

[0014] Optionally, the method further includes: establishing a combined proportional parameter of instantaneous power coefficient and voltage sensitivity coefficient; obtaining the impedance characteristic parameter of the load; and correcting the power allocation weight parameter based on the combined proportional parameter and the impedance characteristic parameter.

[0015] Optionally, the method further includes: obtaining the remaining energy parameters of the flywheel energy storage system; when the remaining energy parameters are lower than a preset energy threshold, extracting the output power characteristic parameters of the diesel generator and the charging demand parameters of the flywheel energy storage; dynamically allocating charging power according to the output power characteristic parameters and the charging demand parameters to obtain flywheel charging ratio parameters; adjusting the input-output ratio of the diesel generator and the flywheel energy storage based on the flywheel charging ratio parameters, and updating the state parameters of the flywheel energy storage system.

[0016] Based on the same inventive concept, this invention also provides a flywheel energy storage system support system based on data center power switching. The system includes: a power status monitoring module, used to collect real-time impedance parameters of each electrical device in the power grid through preset impedance detection nodes, and determine the power supply status of the main power supply based on the real-time impedance parameters to generate power topology status parameters; a dynamic compensation calculation module, used to generate flywheel rotor dynamic torque compensation parameters based on the power topology status parameters and a preset torque threshold; a multi-level power switching module, used to control a preset multi-level power switching matrix to output synchronization phase parameters based on the dynamic torque compensation parameters; and a power distribution control module, used to generate power distribution weight parameters based on the synchronization phase parameters and a preset load priority queue, and update the status parameters of the flywheel energy storage system.

[0017] Compared with the prior art, the present invention has the following advantages:

[0018] 1. This invention can quickly identify abnormal states of the main power supply by collecting impedance parameters of each node of the power grid in real time and generating power topology state parameters. Combined with dynamic torque compensation parameters, it can achieve precise torque control of the flywheel energy storage system, ensuring that the mechanical torque output by the flywheel matches the grid demand in real time during the switching process between the main and backup power supplies, shortening the switching delay to the millisecond level, and avoiding power interruption problems caused by traditional mechanical switching.

[0019] 2. Based on the synchronous phase control of the multi-level power switching matrix and the dynamic weight allocation of the load priority queue, it can automatically compensate for phase changes during power outages, optimize the power output ratio, significantly reduce voltage fluctuations and harmonic interference, ensure continuous and stable power supply to critical loads, and extend the continuous support time of the flywheel energy storage system.

[0020] 3. This invention uses a combined control mechanism of load priority queue and flywheel residual energy to intelligently allocate the input-output ratio of diesel generator and flywheel energy storage during power switching, maximizing the use of the transient compensation capability of flywheel energy storage, reducing the redundant power output of diesel generator, thereby reducing overall energy consumption and extending equipment life.

[0021] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a method for supporting a flywheel energy storage system based on data center power switching, according to an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram showing the changes in theoretical torque requirement parameters and real-time angular velocity data in an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of the phase synchronization adjustment waveform during multi-stage power supply switching according to an embodiment of the present invention.

[0026] Figure 4 This is a schematic diagram of the structure of a flywheel energy storage system support system based on data center power switching according to an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Reference Figure 1 One embodiment of the present invention proposes a flywheel energy storage system support method based on data center power switching. By monitoring the grid topology status in real time through impedance parameters, dynamically adjusting the flywheel torque compensation parameters and multi-level power supply synchronization phase control, and combining the power weight allocation of the load priority queue, it can achieve millisecond-level response, continuous phase transition and power supply continuity guarantee for critical loads during power switching.

[0029] The method described in this embodiment specifically includes:

[0030] Real-time impedance parameters of each electrical device in the power grid are collected by preset impedance detection nodes, and the power supply status of the main power supply is determined based on the real-time impedance parameters to generate power topology status parameters.

[0031] Based on the power topology state parameters and the preset torque threshold, the flywheel rotor dynamic torque compensation parameters are generated;

[0032] Based on the dynamic torque compensation parameters, the preset multi-level power switching matrix is ​​controlled to output synchronous phase parameters.

[0033] Based on the synchronous phase parameters and the preset load priority queue, power allocation weight parameters are generated, and the state parameters of the flywheel energy storage system are updated.

[0034] Specifically, power switching control is achieved through real-time impedance parameter acquisition and grid topology state analysis. The specific principles include: continuously acquiring real-time impedance parameters of electrical equipment using impedance detection devices deployed at key grid nodes; identifying abnormal power supply status of the main power source based on impedance value mutation characteristics; constructing a topology impedance map based on multi-node impedance data; generating a power topology state parameter matrix reflecting the grid connection status using Euler loop analysis; generating dynamic torque compensation parameters through a closed-loop correction algorithm, combined with a preset torque threshold and real-time flywheel rotor angular velocity data, driving the flywheel to adjust mechanical energy output; and finally achieving phase synchronization through carrier phase-shift control of a multi-level power switching matrix, and dynamically allocating output power weights based on load priority queues. This principle integrates grid topology parameter analysis, mechanical dynamics compensation, and power electronic control technology, forming a complete closed-loop control chain from state perception to energy dispatch.

[0035] Optionally, the generation of power topology state parameters includes:

[0036] Real-time impedance parameters of various electrical devices in the power grid are collected by pre-set impedance detection nodes.

[0037] The power supply status of the main power supply is determined based on the real-time impedance parameters, and the power supply status determination result is obtained.

[0038] The real-time impedance parameters of multiple preset impedance detection nodes are obtained to form a topological impedance map;

[0039] Based on the topology impedance map and the power supply status judgment result, power topology status parameters are generated.

[0040] Specifically, the system first continuously acquires real-time impedance parameters of all electrical equipment in the power grid through preset impedance detection nodes. These real-time impedance parameters represent the vector sum of the equivalent resistance and reactance of the equipment in the AC power grid. The impedance detection nodes are deployed at the intersections of main power lines and feature a combined measurement structure of voltage and current transformers, ensuring simultaneous acquisition of voltage amplitude, phase, and current waveform data. The real-time impedance parameters of the node where the main power supply is located are then compared with its historical reference values. If the magnitude of the measured impedance exceeds a preset threshold and exhibits asymmetrical fluctuations, it is determined to be an abnormal power supply state; otherwise, it is considered a normal power supply state. The power supply state judgment result is marked with a binary identifier of 0 or 1. Next, the impedance parameters acquired by each detection node are mapped to coordinates according to the physical connection topology of the power grid. Each node corresponds to a coordinate system node in the graph, and the connections between nodes include impedance parameter vectors, thus forming a topological impedance graph. Finally, Euler loop analysis is performed on the topological impedance graph, and a power topology state parameter matrix P is generated by combining it with the power supply state identifier. The element in the i-th row and j-th column of matrix P is composed of the product of the equivalent impedance of the path from node i to node j and the power supply status factor. A power supply status factor of 1 indicates that the corresponding path is in the normal operating region of the main power supply, while 0 indicates an abnormal disconnection region. The generation process of this parameter matrix satisfies the principle of linear superposition of node admittance matrices in electrical network theory.

[0041] For example, when a single-phase ground fault occurs on the main power supply line due to a lightning strike, the impedance value measured at the detection node suddenly increases to 3.5 times the baseline value. The system first marks the power supply status judgment result as 0, then automatically captures the impedance data of all adjacent nodes and marks the affected area as a gray isolation zone in the topology map. At this time, through Euler loop calculation, it is found that the column vector corresponding to the third feeder in the topology matrix has a sudden change in amplitude, and the system generates a new power topology state parameter matrix accordingly. This matrix isolates the values ​​of the relevant areas of the faulty feeder, while increasing the numerical weight of the backup power path. Through real-time impedance monitoring and map modeling, abnormal power supply areas can be accurately identified within microseconds, automatically constructing the decision basis for isolation and switching, effectively preventing fault propagation, ensuring rapid reconstruction of power supply topology parameters under fault conditions, and providing accurate grid state model support for subsequent power switching.

[0042] Optionally, the generated flywheel rotor dynamic torque compensation parameters include:

[0043] The theoretical torque demand parameters are calculated based on the power topology state parameters.

[0044] Collect real-time angular velocity data of the flywheel rotor, and perform coupling compensation calculations on the real-time angular velocity data and the theoretical torque requirement parameters to generate deviation compensation coefficients;

[0045] The deviation compensation coefficient is corrected in a closed loop to obtain the dynamic torque compensation parameters.

[0046] Specifically, the theoretical torque requirement parameters and real-time angular velocity data are as follows: Figure 2 As shown, the theoretical torque demand parameters are first calculated based on the power topology state parameter matrix. This is achieved by extracting the arithmetic mean of the main diagonal elements of the normally supplied area in the matrix and multiplying it by the flywheel base torque coefficient. The flywheel base torque coefficient is retrieved from a flywheel mechanical characteristic parameter library, which was obtained through offline experimental calibration. This step ensures that the torque reference value adaptively adjusts with the power grid topology state. Next, real-time angular velocity data of the flywheel rotor is acquired using a photoelectric encoder, with a sampling frequency of no less than 2kHz. The deviation compensation coefficient is calculated using the theoretical torque demand parameters and the real-time angular velocity. ,have:

[0047] ;

[0048] In the formula, These are the theoretical torque requirement parameters. For real-time angular velocity, This is a dynamic response factor, adjustable within the range of 0.8 to 1.2. The inertia conversion factor is determined by the product of the flywheel's moment of inertia and the rated angular velocity. The deviation compensation factor represents the instantaneous difference between the current angular velocity and the theoretical requirement. The deviation compensation factor is then input into a closed-loop corrector with PI control characteristics. The corrector generates corrected dynamic torque compensation parameters based on the accumulated amount and rate of change of the deviation compensation factor. In the closed-loop correction calculation, the proportional coefficient is taken from the preset value table of the flywheel control system, and the integral time is dynamically adjusted according to the real-time angular velocity gradient. This process ensures that the response speed of the output torque compensation matches the system inertia, avoiding overshoot oscillations.

[0049] For example, when a power drop of 30 occurs in the main power grid, the theoretical torque demand parameter increases by 32 due to the reduction in the number of power supply areas in the topology matrix. The flywheel rotor's real-time angular velocity drops to 88% of its rated value due to the sudden load increase. The system calculates the instantaneous deviation compensation coefficient. The dynamic torque compensation parameter, calculated as two-thirds of the difference between the current demand and the measured value, is increased to 1.5 times the base value after integral correction by the closed-loop controller. This compensation parameter drives the flywheel energy storage system to complete torque replenishment within 0.3 seconds. Through dynamic coupling and matching between theoretical demand and actual conditions, the impact of load fluctuations on the flywheel angular velocity can be quickly identified. Furthermore, the response lag caused by system inertia is eliminated through a closed-loop feedback mechanism, ensuring that the mechanical torque output by the flywheel precisely matches changes in grid demand. This effectively suppresses speed fluctuations caused by power switching and guarantees a smooth transition in power output.

[0050] Optionally, the calculation of theoretical torque demand parameters based on the power topology state parameters includes:

[0051] Obtain historical load fluctuation parameters and historical power demand parameters, and establish a historical dataset;

[0052] Using real-time load fluctuation parameters as input and real-time power demand parameters as output, a neural network model is established and trained using the historical dataset to obtain a real-time power demand prediction model.

[0053] The real-time load fluctuation parameters are input into the real-time power demand prediction model to obtain the real-time power demand parameters;

[0054] Using the real-time power demand parameters as constraints, the theoretical torque demand parameters are calculated using the power topology state parameters.

[0055] Specifically, the process of calculating theoretical torque demand parameters based on power topology state parameters is as follows: First, historical load fluctuation parameters and historical power demand parameters are retrieved from the system database. The historical load fluctuation parameters are composed of the product of the standard deviation of the effective voltage value and the rate of change of current, while the historical power demand parameters are recorded using the moving average of the effective power sample values. The historical load fluctuation parameters and historical power demand parameters are aligned according to time series and stored in a historical dataset, with each data sample containing parameter records for three complete power frequency cycles. Then, a three-layer feedforward neural network model is constructed, defining its input layer nodes corresponding to the components of the real-time load fluctuation parameters, and its output layer nodes corresponding to the predicted values ​​of the real-time power demand parameters. The intermediate hidden layer nodes use the sigmoid activation function. During training, the historical load fluctuation parameters of each sample in the historical dataset are used as input features, and the historical power demand parameters are used as labeled targets. Model training uses the gradient descent method with momentum, with the momentum coefficient set to 0.9. An early stop mechanism is triggered when the prediction error decreases by less than 0.5 for three consecutive training cycles. The trained model is then written into the real-time power demand prediction module. When the real-time load fluctuation parameters for the current period are obtained, they are input into this prediction model to obtain the real-time power demand parameters. The physical meaning of the real-time power demand parameter represents the power baseline value that the system needs to maintain within the subsequent 2-second time window. Finally, the theoretical torque demand parameter is calculated based on the average value of the main diagonal elements of the power topology state parameter matrix and the real-time power demand parameter. ,have:

[0056] ;

[0057] In the formula, The average value of the main diagonal elements of the power topology state parameter matrix. For real-time power demand parameters, It is composed of the product of the flywheel rotor radius and the electromagnetic torque coefficient, and can be directly obtained from the flywheel physical parameter table. The square root operation can be understood as a stabilization process for power demand, similar to the operating principle of smoothing out the peak values ​​of fluctuating signals.

[0058] For example, a data center experiences a sudden load surge during summer air conditioning startup, causing real-time load fluctuation parameters to reach peak levels. The system retrieves historical load and power data for the corresponding periods over the past three months to build a training set, and the neural network converges after 256 iterations. When the current period's real-time load fluctuation parameter value is input, the prediction module outputs a real-time power demand parameter of 124, the historical baseline value. The average value of the main diagonal elements of the power topology parameter matrix for this period is 6 lower than normal due to offline maintenance of some equipment. The system calculates the theoretical torque demand parameter as 118 of the standard operating condition. This torque parameter, through subsequent control steps, increases and stabilizes the flywheel rotor torque at the target value. By combining historical data training with real-time parameter prediction, the nonlinear relationship between load fluctuations and power demand can be accurately captured. The neural network model compensates for the shortcomings of traditional linear interpolation methods, ensuring that the theoretical torque setting conforms to both the current power grid topology and the predicted power demand, avoiding the dual risks of insufficient power supply due to excessively low torque setting or equipment overload due to excessively high torque setting.

[0059] Optionally, the step of controlling the preset multi-level power switching matrix output synchronization phase parameters based on the dynamic torque compensation parameters includes:

[0060] Based on the dynamic torque compensation parameters, the carrier phase shift timing is adjusted to generate a synchronous phase control signal;

[0061] The synchronization phase control signal is broadcast to the node terminal of the preset multi-level power switching matrix, and the synchronization phase parameters are output.

[0062] Optionally, the method of broadcasting the synchronization phase control signal to the node terminal of a preset multi-level power switching matrix, and outputting synchronization phase parameters, includes:

[0063] Based on the power supply status judgment result, the redundant bridge arms of the main circuit and the backup power supply are switched on and off to obtain the switching result.

[0064] Based on the switching result, the duty cycle of the synchronization phase control signal is adjusted by carrier phase-shift PWM control and synchronized to the node terminal of the preset multi-level power switching matrix to output the synchronization phase parameters.

[0065] Specifically, such as Figure 3 As shown, the dynamic torque compensation parameters are first obtained, which characterize the required torque compensation level of the flywheel system. Phase adjustment parameters are then defined. for:

[0066] ;

[0067] in, These are dynamic torque compensation parameters. The preset phase-shift gain coefficient is taken from the power switching device control system parameter table. Phase adjustment parameters. The physical meaning is the phase shift that the carrier signal needs to be adjusted, and the direction of phase adjustment is determined by the sign of the torque compensation parameters. When the power supply status judgment result indicates a main power supply failure, the control center sends a third-order trigger pulse sequence to the redundant bridge arm. The redundant bridge arm is composed of an H-bridge circuit made of IGBT devices. The timing logic of the trigger pulse is as follows: if the feedback current of the main circuit power supply is lower than the rated value by 20 for 5 milliseconds, then the fourth bridge arm on the backup power supply side is closed and the second bridge arm of the main power supply is disconnected. The signal indicating this switching is completed is encoded into a four-dimensional state vector, with each dimension corresponding to the conduction state of the bridge arm. Subsequently, the phase adjustment parameters are used... Calculate the carrier phase-shifted PWM duty cycle :

[0068] ;

[0069] In the formula As the baseline duty cycle, This represents the number of cascaded units in a multi-stage power supply matrix. The phase adjustment in the numerator of this formula is linearly related to the duty cycle, and the calculation process uses dimensionless parameters in radians to avoid dimensional conflicts. Finally, the calculated duty cycle value is distributed to each node terminal through an optical fiber synchronization ring network. Each terminal synchronizes its local carrier phase to the phase shift reference of the main controller to obtain the synchronization phase parameter.

[0070] For example, when the dynamic torque compensation parameter suddenly increases to 75% of the maximum compensation amount, the system calculates the phase adjustment parameter. for Radius. A momentary short circuit in the main power supply triggers the power supply status judgment, and the backup power supply bridge arm switches within 1 millisecond. At this time, the duty cycle of the carrier phase-shift PWM increases from the reference value of 0.6 to the correction value. The duty cycle parameter controls the stepped phase synchronization waveform output by the power switching matrix, i.e., the synchronization phase parameter, with the phase difference between each node terminal precisely locked at 120 degrees. Through the direct correlation between torque compensation parameters and phase adjustment, the power output phase during power switching changes continuously and gradually, avoiding waveform distortion caused by traditional hard switching. The timing coordination mechanism of redundant bridge arm switching and phase adjustment effectively bridges the electrical phase vacuum period during main / standby power switching, ensuring the three-phase symmetry of the inverter output waveform and suppressing torque oscillations in motor-type loads caused by abrupt phase changes.

[0071] Optionally, the step of generating power allocation weight parameters based on the synchronization phase parameters and the preset load priority queue, and updating the state parameters of the flywheel energy storage system includes:

[0072] Create a timestamp-based load priority queue and label the response level parameters of core devices;

[0073] Obtain the instantaneous power demand, and calculate the power allocation weight parameter based on the response level parameter and the instantaneous power demand;

[0074] The state parameters of the flywheel energy storage system are updated based on the power allocation weight parameters.

[0075] Specifically, a dynamic load priority queue is first established using millisecond-level timestamps as indexes. Each load device is assigned a unique response level parameter, with a value ranging from 1 to 10. Core devices such as server racks have a response level no lower than 8, while auxiliary devices such as environmental monitoring sensors have a response level no higher than 3. The response level parameter is obtained from a preset field in the asset list of the device operation and maintenance management system, and its response level automatically increases by 2 levels when a device is in an alarm state. Next, the system's instantaneous total power demand is collected. This value is obtained by taking the dot product of the three-phase current vector sum and voltage vector measured by the bus current sensor array, with a sampling period of 200 microseconds. Power allocation weight parameters are then defined for device i. ,have:

[0076] ;

[0077] In the formula, For the response level parameters of device i, This represents the average active power demand, or instantaneous power demand, of device i within the current sampling window, directly measured by the energy metering chip. The sum of the denominators during the calculation process... Ensure that the sum of the weights of all devices is normalized to 1 to satisfy the allocation ratio constraint. Finally, input the weight parameters into the state update module of the flywheel energy storage system to adjust the multi-channel power allocation ratio at its inverter output. During the state update, a Kalman filter algorithm is used to fuse the current power allocation parameters with the flywheel speed feedback data to generate a new system state vector, which includes the estimated remaining energy value and the output stability index.

[0078] For example, when a data center experiences a power outage, the response level parameter of the core server rack increases from 8 to 10, with an instantaneous power demand of 50kW. Simultaneously, the instantaneous power demand of the backup air conditioning system is 30kW, but the response level parameter remains at 5. The power allocation weight parameter for the server rack, calculated using the formula, is 0.87, and the power allocation weight parameter for the air conditioning system is 0.13. This weight allocation concentrates 86% of the flywheel energy storage system's output power in the server rack, reserving only 14% for maintaining basic environmental system operations. Through a joint weighting mechanism of the squared response level and power demand, the weight advantage of core equipment is amplified exponentially during sudden power outages, ensuring critical loads receive an exponentially increased power supply guarantee, while preventing low-priority equipment from excessively consuming energy storage resources, significantly extending the system's sustainable power supply time in emergency mode.

[0079] Optionally, the method further includes:

[0080] Establish a combined proportional parameter for the instantaneous power coefficient and the voltage sensitivity coefficient;

[0081] Obtain the impedance characteristics of the load;

[0082] The power allocation weighting parameter is corrected based on the combined proportional parameter and the impedance characteristic parameter.

[0083] Specifically, the instantaneous power factor is first defined as the ratio of the instantaneous rate of change of power demand to the rated power at the current moment. The instantaneous rate of change of power demand is calculated by the difference in total power within two consecutive sampling windows, and the rated power is obtained from the equipment nameplate parameter database. The voltage sensitivity coefficient is defined according to the voltage fluctuation tolerance threshold specified in the equipment operation manual, with a numerical range set as an increasing discrete variable from 0.1 to 1.0. For every 5 percentage point reduction in the allowable voltage fluctuation range, the corresponding voltage sensitivity coefficient value increases by 0.1. The combined proportional parameter is generated by multiplying the instantaneous power factor and the voltage sensitivity coefficient. Next, the equivalent impedance parameter of the load is measured online using an impedance tester, expressed as the vector magnitude of the resistive and reactive components, in ohms. The corrected power allocation weight parameter... ,have:

[0084] ;

[0085] In the formula, For the combined proportional parameters, This is the load admittance, which is the reciprocal of the load's equivalent impedance parameter. The physical meaning of this calculation lies in considering both the urgency of power demand and the voltage sensitivity of the equipment, while also adapting to the electrical characteristics of the load itself. The admittance term automatically assigns higher weight to low-impedance devices, similar to the natural current distribution pattern in a circuit. The parameters are obtained as follows: the instantaneous power coefficient is obtained from the real-time power sampling module, the voltage sensitivity coefficient is taken from the equipment parameter configuration table, and the load equivalent impedance parameter is dynamically measured by an online impedance sweep frequency measurement device.

[0086] For example, when a batch of server clusters experiences a sudden surge in instantaneous power demand due to the startup of batch tasks, and the instantaneous rate of change of power demand reaches 30% of the rated power, the instantaneous power factor is calculated to be 0.3. The voltage sensitivity factor of the server is set to 0.8. At this time, the combined proportional parameter is equal to the product of 0.3 and 0.8, which is 0.24. The measured server rack impedance, i.e., the equivalent load impedance parameter, is 2, corresponding to an admittance of 0.5. If the original power allocation weight parameter is 0.6, the corrected power allocation weight parameter is calculated to be 0.072. This adjustment significantly reduces the power allocation weight of the server cluster, but at the same time, the weight of low-impedance UPS devices in adjacent areas is automatically increased. The beneficial effect of this verification example is that by introducing dual modeling of impedance characteristic parameters and dynamic demand factors, it can intelligently suppress the overload risk of highly sensitive low-impedance devices during power surges, prompting the system power allocation to automatically tend towards a path of electrical stability, and avoiding resonant oscillation problems caused by impedance mismatch of local devices.

[0087] Optionally, the method further includes:

[0088] Obtain the remaining energy parameters of the flywheel energy storage system;

[0089] When the remaining energy parameter is lower than the preset energy threshold, the output power characteristic parameter of the diesel generator and the charging demand parameter of the flywheel energy storage are extracted.

[0090] Based on the output power characteristic parameters and the charging demand parameters, the charging power is dynamically allocated to obtain the flywheel charging ratio parameters.

[0091] Based on the flywheel charging ratio parameters, adjust the input-output ratio of the diesel generator and the flywheel energy storage, and update the status parameters of the flywheel energy storage system.

[0092] Specifically, when the remaining energy parameter of the flywheel energy storage system falls below a preset threshold, the system activates a dynamic charging ratio mechanism. First, a Hall sensor array pre-installed on the flywheel shaft collects the rotor's rotational count, and this, combined with the energy storage density parameter at rated speed, calculates the remaining energy parameter. The calculation of the remaining energy parameter follows the mechanical kinetic energy formula, which is equal to half the product of the rotor's moment of inertia and the square of its angular velocity. The rotor's moment of inertia is given by the flywheel design parameters, and the angular velocity is measured in real-time by a photoelectric encoder. When the remaining energy parameter falls below the preset threshold, the system activates the diesel generator's intelligent charging mode. The diesel generator's output power characteristic parameters include its current active power output, maximum continuous power, and transient overload capacity coefficient, which are read in real-time through the generator controller's Modbus protocol interface. The flywheel charging demand parameter is the remaining energy gap. :

[0093] ,

[0094] in For rated energy storage, This stores energy for the current instantaneous moment. When dynamically allocating charging power, a ratio coefficient, or flywheel charging ratio parameter, is defined as the ratio of available remaining power to charging demand. For the flywheel charging ratio parameter... ,have:

[0095] ;

[0096] in This represents the current total system load power. This represents the current output active power. When the flywheel charging ratio parameter is greater than 1, the diesel generator output is adjusted according to the proportional flywheel charging ratio parameter to the sum of the current system total load power and the flywheel charging demand parameter; if the flywheel charging ratio parameter is less than 1, the charging power is set to... This triggers a downgraded charging protocol. Ultimately, the flywheel charging ratio parameter is mapped to an adjustment command of 0-100% to control the diesel generator set actuator to adjust the fuel injection quantity, while simultaneously adjusting the chopper duty cycle of the flywheel charging circuit to achieve synchronous matching of input and output power.

[0097] For example, the system detects when the remaining energy stored in the flywheel drops to 25% of its rated value. The system faces an energy gap. At this point, the diesel generator output is 200kW, and the total system load is 150kW. The calculated available surplus power is 200-150=50kW, with a ratio coefficient of 50 / 55 less than 1. The system increases the diesel generator output to 150+50=200kW to charge the flywheel at full power, replenishing the energy gap within 0.5 hours. By dynamically balancing the energy gap and generator capacity in real time, the system maximizes the use of redundant diesel generator power while ensuring power supply to the existing load. This avoids ineffective no-load operation of the generator set and ensures rapid recharging of the flywheel energy storage to a safe operating threshold, significantly improving the system's sustained support capability against continuous power outages.

[0098] Based on the same inventive concept, such as Figure 4 As shown, the present invention also provides a flywheel energy storage system support system based on data center power switching, the system comprising:

[0099] The power status monitoring module is used to collect real-time impedance parameters of each electrical device in the power grid through preset impedance detection nodes, and to determine the power supply status of the main power supply based on the real-time impedance parameters, thereby generating power topology status parameters.

[0100] The dynamic compensation calculation module is used to generate dynamic torque compensation parameters for the flywheel rotor based on the power topology state parameters and the preset torque threshold.

[0101] A multi-stage power switching module is used to control a preset multi-stage power switching matrix to output synchronous phase parameters based on the dynamic torque compensation parameters.

[0102] The power distribution control module is used to generate power distribution weight parameters based on the synchronization phase parameters and the preset load priority queue, and to update the state parameters of the flywheel energy storage system.

[0103] It should be noted that the formulas described above, through the principle of dimensional consistency and mathematical standardization methods (such as normalization, dimensionless parameter conversion, or unit system unification), can translate physical quantities with different properties into unitless standard values ​​or parameters that can be superimposed in the same dimension. This eliminates the interference of different dimensions on the computational logic, allowing the formulas to retain the original data distribution characteristics while possessing mathematical rationality and adaptability to objective laws. These are conventional technical methods and will not be elaborated further. The electrical connections between the various units described above do not necessarily represent direct or indirect connections; any indirect connection method is applicable to the embodiments of this invention as long as it achieves the purpose of this invention. The above descriptions are merely exemplary embodiments of this invention and should not be construed as limiting the scope of this invention.

[0104] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for supporting a flywheel energy storage system based on data center power switching, characterized in that, The method includes: Real-time impedance parameters of each electrical device in the power grid are collected by preset impedance detection nodes, and the power supply status of the main power supply is determined based on the real-time impedance parameters to generate power topology status parameters. Based on the power topology state parameters and the preset torque threshold, the flywheel rotor dynamic torque compensation parameters are generated; The synchronous phase parameters are output based on the preset multi-level power switching matrix controlled by the dynamic torque compensation parameters. This includes: adjusting the carrier phase shift timing based on the dynamic torque compensation parameters to generate a synchronous phase control signal; controlling the switching of the redundant bridge arms of the main circuit and the backup power supply based on the power supply status judgment result to obtain the switching result; and adjusting the duty cycle of the synchronous phase control signal using carrier phase shift PWM control based on the switching result, and synchronizing it to the node terminal of the preset multi-level power switching matrix to output the synchronous phase parameters. Based on the synchronization phase parameters and the preset load priority queue, power allocation weight parameters are generated, and the state parameters of the flywheel energy storage system are updated. This includes: after outputting the synchronization phase parameters, creating a timestamp-based load priority queue and marking the response level parameters of the core equipment; obtaining the instantaneous power demand, calculating the power allocation weight parameters based on the response level parameters and the instantaneous power demand; and updating the state parameters of the flywheel energy storage system based on the power allocation weight parameters.

2. The method for supporting a flywheel energy storage system based on data center power switching according to claim 1, characterized in that, The generated power topology state parameters include: Real-time impedance parameters of various electrical devices in the power grid are collected by pre-set impedance detection nodes. The power supply status of the main power supply is determined based on the real-time impedance parameters, and the power supply status determination result is obtained. The real-time impedance parameters of multiple preset impedance detection nodes are obtained to form a topological impedance map; Based on the topology impedance map and the power supply status judgment result, power topology status parameters are generated.

3. The method for supporting a flywheel energy storage system based on data center power switching according to claim 2, characterized in that, The generated flywheel rotor dynamic torque compensation parameters include: The theoretical torque demand parameters are calculated based on the power topology state parameters. Collect real-time angular velocity data of the flywheel rotor, and perform coupling compensation calculations on the real-time angular velocity data and the theoretical torque requirement parameters to generate deviation compensation coefficients; The deviation compensation coefficient is corrected in a closed loop to obtain the dynamic torque compensation parameters.

4. The method for supporting a flywheel energy storage system based on data center power switching according to claim 3, characterized in that, The theoretical torque demand parameters calculated based on the power topology state parameters include: Obtain historical load fluctuation parameters and historical power demand parameters, and establish a historical dataset; Using real-time load fluctuation parameters as input and real-time power demand parameters as output, a neural network model is established and trained using the historical dataset to obtain a real-time power demand prediction model. The real-time load fluctuation parameters are input into the real-time power demand prediction model to obtain the real-time power demand parameters; Using the real-time power demand parameters as constraints, the theoretical torque demand parameters are calculated using the power topology state parameters.

5. The method for supporting a flywheel energy storage system based on data center power switching according to claim 3, characterized in that, The method further includes: Establish a combined proportional parameter for the instantaneous power coefficient and the voltage sensitivity coefficient; Obtain the impedance characteristics of the load; The power allocation weighting parameter is corrected based on the combined proportional parameter and the impedance characteristic parameter.

6. The method for supporting a flywheel energy storage system based on data center power switching according to claim 5, characterized in that, The method further includes: Obtain the remaining energy parameters of the flywheel energy storage system; When the remaining energy parameter is lower than the preset energy threshold, the output power characteristic parameter of the diesel generator and the charging demand parameter of the flywheel energy storage are extracted. Based on the output power characteristic parameters and the charging demand parameters, the charging power is dynamically allocated to obtain the flywheel charging ratio parameters. Based on the flywheel charging ratio parameters, adjust the input-output ratio of the diesel generator and the flywheel energy storage, and update the status parameters of the flywheel energy storage system.

7. A support system for a flywheel energy storage system based on data center power switching, applied to the support method for a flywheel energy storage system based on data center power switching as described in any one of claims 1-6, characterized in that, The system includes: The power status monitoring module is used to collect real-time impedance parameters of each electrical device in the power grid through preset impedance detection nodes, and to determine the power supply status of the main power supply based on the real-time impedance parameters, thereby generating power topology status parameters. The dynamic compensation calculation module is used to generate dynamic torque compensation parameters for the flywheel rotor based on the power topology state parameters and the preset torque threshold. A multi-level power switching module is used to control a preset multi-level power switching matrix to output synchronous phase parameters based on the dynamic torque compensation parameters. This includes: adjusting the carrier phase-shift timing based on the dynamic torque compensation parameters to generate a synchronous phase control signal; controlling the switching of redundant bridge arms between the main circuit and the backup power supply based on the power supply status judgment result to obtain a switching result; and adjusting the duty cycle of the synchronous phase control signal using carrier phase-shift PWM control based on the switching result, and synchronizing it to the node terminals of the preset multi-level power switching matrix to output the synchronous phase parameters. The power allocation control module is used to generate power allocation weight parameters based on the synchronization phase parameters and a preset load priority queue, and to update the state parameters of the flywheel energy storage system. This includes: after outputting the synchronization phase parameters, creating a timestamp-based load priority queue and marking the response level parameters of the core equipment; obtaining the instantaneous power demand; calculating the power allocation weight parameters based on the response level parameters and the instantaneous power demand; and updating the state parameters of the flywheel energy storage system based on the power allocation weight parameters.

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